Apparatus and Methods for Wafer to Wafer Bonding

ABSTRACT

A method includes having a first wafer bonding recipe and a model of a wafer bonding process, the model comprising an input indicative of a physical parameter of a first wafer to be bonded to a second wafer and configured to output a wafer bonding recipe based on the physical parameter of the first wafer; obtaining measurements of the first wafer to obtain the physical parameter of the first wafer; generating, by the model, the first wafer bonding recipe based on the physical parameter of the first wafer; and bonding the first wafer to the second wafer in accordance with the first wafer bonding recipe to produce a first post-bond wafer.

TECHNICAL FIELD

The present invention relates generally to substrate processing, and, inparticular embodiments, to apparatus and methods of wafer to waferbonding.

BACKGROUND

Wafer to wafer bonding is a packaging technology used in the productionof microelectromechanical systems (MEMS), nanoelectromechanical systems(NEMS), microelectronics and optoelectronics. Fusion bonding (alsocommonly referred to as direct bonding) is a wafer to wafer bondingprocess that does not require any additional intermediate layers. Infusion bonding, two wafers (e.g., a top wafer and a bottom wafer) arebrought together and the two wafers begin to bond as the surfaces of thewafers begin to touch, forming a post-bond wafer. Annealing thepost-bond wafers at elevated temperatures increases the bond strengthbetween the two wafers and forms a fusion bonded wafer.

FIG. 1A illustrates a portion of a wafer to wafer bonding process 100for bonding two wafers using a conventional fusion bonding process. Infusion bonding, a top wafer 105 and a bottom wafer 110 are bonded toform a post-bond wafer 115. Fusion bonding also includes annealing thepost-bond wafers to strengthen the bond between the two wafers, which isnot shown in FIG. 1A.

The quality of a wafer to wafer bond may be dependent upon a variety offactors that may be grouped into two distinct categories: wafercharacteristics and process conditions. Examples of wafercharacteristics include wafer flatness, wafer smoothness, wafercleanliness, wafer materials, and so on, while examples of processconditions include bonding temperature, environmental conditions in abonding chamber where the wafer to wafer bonding is performed, appliedforce, and so on.

FIG. 1B illustrates a side-view of post-bond wafer 115. As shown in FIG.1B, top wafer 105 and bottom wafer 110 are well-bonded together andthere is a smooth interface between the two wafers. FIG. 1C illustratesa side view of post-bond wafer 130 highlighting a poor bond between thetwo wafers. As shown in FIG. 1C, top wafer 105 has a concave profile,which leads to the formation of a gap 135 when top wafer 105 and bottomwafer no are bonded.

SUMMARY

In accordance with an embodiment of the invention, a method is provided.The method includes: having a first wafer bonding recipe and a model ofa wafer bonding process, the model comprising an input indicative of aphysical parameter of a first wafer to be bonded to a second wafer andconfigured to output a wafer bonding recipe based on the physicalparameter of the first wafer; obtaining measurements of the first waferto obtain the physical parameter of the first wafer; generating, by themodel, the first wafer bonding recipe based on the physical parameter ofthe first wafer; and bonding the first wafer to the second wafer inaccordance with the first wafer bonding recipe to produce a firstpost-bond wafer.

In accordance with another embodiment, a method is provided. The methodincludes: having a model of a wafer bonding process, the modelcomprising an input indicative of a physical parameter of a firstpost-bond wafer and configured to output a wafer bonding recipe based onthe physical parameter of the first post-bond wafer; bonding a firstwafer to a second wafer in accordance with a wafer bonding recipe, toform the first post-bond wafer; obtaining measurements of the firstpost-bond wafer to obtain the physical parameter of the first post-bondwafer; generating, by the model, the first wafer bonding recipe inaccordance with the physical parameter of the first post-bond wafer; andbonding a third wafer to a fourth wafer in accordance with the firstwafer bonding recipe, to form a second post-bond wafer.

In accordance with still another embodiment of the invention, aprocessing system is provided. The processing system includes: anon-transitory computer-readable storage medium comprising instructionsthat when executed cause a processor of a computing device to performoperations in coordination with a semiconductor wafer fabricationprocess, the instructions comprising: having a first wafer bondingrecipe and a model of a wafer bonding process, the model comprising aninput indicative of a physical parameter of a first wafer to be bondedto a second wafer and configured to output a wafer bonding recipe basedon the physical parameter of the first wafer; obtaining measurements ofthe first wafer to obtain the physical parameter of the first wafer;generating, by the model, the first wafer bonding recipe based on thephysical parameter of the first wafer; and bonding the first wafer tothe second wafer in accordance with the first wafer bonding recipe toproduce a first post-bond wafer.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of the present invention, and theadvantages thereof, reference is now made to the following descriptionstaken in conjunction with the accompanying drawings, in which:

FIG. 1A illustrates a portion of a wafer to wafer bonding process forbonding two wafers using a conventional fusion bonding process;

FIGS. 1B-1C illustrate side-views of post-bond wafers;

FIG. 2 illustrates a conventional wafer fusion bonding process;

FIG. 3 illustrates a wafer bonding system that utilizes wafer metrologydata to tune a wafer bonding recipe according to the example embodimentspresented herein;

FIG. 4 illustrates an example wafer processing tool according to exampleembodiments presented herein;

FIG. 5 illustrates a wafer fusion bonding process with example feedbackoperation according to example embodiments presented herein;

FIG. 6 illustrates a wafer fusion bonding process with a first examplefeedforward operation according to example embodiments presented herein;

FIG. 7 illustrates a wafer fusion bonding process with a second examplefeedforward operation according to example embodiments presented herein;

FIG. 8 illustrates a flow diagram of example operations occurring in awafer bonding process with feedforward optimization of the wafer bondingrecipe according to example embodiments presented herein;

FIG. 9A illustrates a flow diagram of first example process creating amodel of a wafer bonding process using a finite element modeling (FEM)technique enhanced with semi-empirical modeling according to exampleembodiments presented herein;

FIG. 9B illustrates a flow diagram of second example process in creatinga model of a wafer bonding process using FEM techniques enhanced withsemi-empirical modeling and calibration according to example embodimentspresented herein;

FIG. 10 illustrates a flow diagram of example operations occurring increating a model of a wafer bonding process using fingerprintingfunctions according to example embodiments presented herein;

FIG. 11 illustrates a flow diagram of operations occurring in ageneration of a wafer bonding recipe with feedforward based optimizationof the wafer bonding recipe according to example embodiments presentedherein;

FIG. 12 illustrates a flow diagram of example operations occurring in awafer bonding process with feedback optimization of the wafer bodingrecipe according to example embodiments presented herein;

FIG. 13 illustrates a flow diagram of an example process in creating amodel of a wafer bonding processing using feedback data according toexample embodiments presented herein; and

FIG. 14 illustrates a flow diagram of operations occurring in ageneration of a wafer bonding recipe with feedback based optimization ofthe wafer bonding recipe according to example embodiments presentedherein.

Corresponding numerals and symbols in the different figures generallyrefer to corresponding parts unless otherwise indicated. The figures aredrawn to clearly illustrate the relevant aspects of the embodiments andare not necessarily drawn to scale. The edges of features drawn in thefigures do not necessarily indicate the termination of the extent of thefeature.

DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

The making and using of various embodiments are discussed in detailbelow. It should be appreciated, however, that the various embodimentsdescribed herein are applicable in a wide variety of specific contexts.The specific embodiments discussed are merely illustrative of specificways to make and use various embodiments, and should not be construed ina limited scope.

Various techniques, as described herein, pertain to wafer fusion bondingusing wafer metrology data to dynamically control the wafer bondingrecipe to control post-bond wafer distortion. As an example, the wafermetrology data is provided to a model of the wafer bonding process todetermine process conditions of the wafer bonding process that ispredicted to produce post-bond wafers that meet a post-bond distortionthreshold, which may then be applied to the actual bonding of thewafers. In other words, the wafer metrology data is used to tune a waferbonding recipe that will produce post-bond wafers that meet thepost-bond distortion threshold.

FIG. 2 illustrates a conventional wafer fusion bonding process 200. Asshown in FIG. 2, a wafer processing tool 205 fusion bonds a first topwafer 210 and a first bottom wafer 220 using a process of record (POR)wafer bonding recipe to form a first post-bond wafer 230. Typically, aplurality of top wafers is bonded with a plurality of bottom wafers toform a plurality of post-bond wafers. As an example, second top wafer212 is bonded with second bottom wafer 222 to form second post-bondwafer 232, and N-th top wafer 214 is bonded with N-th bottom wafer 224to form N-th post-bond wafer 234. The annealing stage of conventionalwafer fusion bonding process 200 is not shown in FIG. 2.

In the conventional wafer fusion bonding process, the same POR waferbonding recipe is used for all of the top and bottom wafers, independentof if the top and bottom wafers belong to the same respective wafer lotsor not.

Wafers are commonly processed in lots. One lot may contain up to 25wafers with one of the 25 wafers being a monitor wafer that may or maynot be used in the bonding process, for example. Inter-lot wafervariation is a significant contributor to post-bond wafer distortion.Production wafers usually vary from one lot to another, possibly due toprocessing conditions due to process control variations of prior steps,intrinsic wafer deformation, or other less obvious factors. Thevariation between wafers is referred to as the shape variation. Theshape variation can be local, meaning an averaged surface map of one lotof wafers looks entirely different from an averaged surface map ofanother lot of wafers, with one standard deviation of the shapevariation ranging from 1 μm up to 5 μm in 300 mm wafers with logicdevices, for example. The shape variation may be different in waferswith memory devices. The shape variation can also be global, meaning thepeak-to-valley variation can be on the order of 10 μm or more from lotto lot. For memory applications, it is possible that the shape variationis much larger. Intra-lot variation is usually small, except in the caseof split lots. A split lot means that subsets of the wafers within a lotare separated and are processed differently. When split lots are notconsidered, the intra-lot variation is typically far less than 10% ofthe inter-lot variation, however actual values may differ depending onthe wafers being considered.

The shape variation is directly correlated to in-plane distortion (orsimply referred to as “distortion”). The in-plane distortion of a waferis defined as deviation along the major plane of the wafer. As anexample, high spots (e.g., peaks) and low spots (e.g., valleys) on thesurface of a wafer are in-plane distortions of the wafer. Observationshave shown that 1 μm of shape variation may be attributed toapproximately 5 nm of in-plane distortion. Therefore, in-planedistortion is a critical metric for wafer bonding process performance.

One way to evaluate the quality of a wafer bonding process is to examinethe post-bond wafer. For example, if the shape variation of thepost-bond wafers is high, then the wafer bonding process may not be welltuned to the wafers being bonded. While if the shape variation of thepost-bond wafers is low, then the wafer bonding process may be welltuned to the wafers being bonded. For another example, if the shapevariation of the post-bond wafers is low for intra-lot wafers but ishigh for inter-lot wafers, then the wafer bonding process may be welltuned for the particular lot of wafers, but not for a different lot ofwafers.

In the case where both wafers being bonded are patterned (i.e., bothwafers include devices and/or structures), the alignment between thepatterns of the two substrates along the bonding interface is important.Alignment marks on each patterned layer may be measured by a scanner ata post-bonding step. The scanner measurement step may not be necessarilyperformed immediately after the bonding step. As an example, thepost-bond wafer may be annealed in a processing chamber at elevatedtemperature (hundreds of degrees Celsius, for example) to improve thebonding strength between the two layers. As another example, one of thewafers may be thinned by grinding to expose the bonding interface. Otherless obvious steps may also exist in between the wafer bonding step andthe post-bond metrology step. In any case, the absolute shift betweenthe two wafers of the post-bond wafer is of critical importance. Typicalpost-bond alignment error at one standard deviation variation can beabout 30 nm, for example.

In the case where only one wafer is patterned, the post-bond distortionof the patterned wafer is important. The post-bond distortion may be afunction of the shape variation of both pre-bond wafers, as well as thewafer bonding process. Some important factors that determine thepost-bond distortion include the temperature distribution of the wafers,chucking forces on the wafers, adhesion between the wafers, surfaceroughness of the bonding surfaces of the two wafers, chuck flatness,spacing between the wafers prior to the bonding process, pre-cleaning ofthe two target bonding surfaces, and many other factors not listed here.After bonding, the post-bond distortion may be measured by the scanneras well, similar to the case where both wafers are patterned. However,in this case, the post-bond distortion is defined as the absolutedeviation from an ideal distortion. The ideal distortion may be definedby the scanner. There are many models that the scanner may adopt todefine the ideal distortion. Traditionally, the scanner uses a 6-termmodel for the ideal distortion. The 6 terms include magnification andtilt of the distortion surface maps. More recently, models have becomemore complicated, for example, a 33-term model has been proposed. Somestate-of-the-art technology support free-form models (thousands ofterms). A challenge that arises from selecting one of the highcomplexity models is that the time and cost required to create suchmodel becomes unfeasible in the production environment.

Furthermore, post-bond distortion is becoming an increasingly difficultproduction target to meet. As an example, the current state-of-the-artpost-bond distortion threshold has a one standard deviation of 10 nm orless, with customer requirements for post-bond distortion continuing todecrease in both the near term and the long term.

Because the conventional wafer bonding process shown in FIG. 2 uses asingle POR wafer bonding recipe to bond wafers (independent of whetheror not the wafers being bonded are intra-lot or inter-lot wafers), thebonding process related factors that impact post-bond distortion (i.e.,the factors that are controlled by the POR wafer bonding recipe, such astemperature distribution of the wafers, chucking forces on the wafers,chuck flatness, spacing between the wafers prior to the bonding process,pre-cleaning of the two target bonding surfaces, and so on) remainfixed, while shape variation of the wafers changes between individualwafers and wafer lots. Therefore, inter-lot post-bond distortion controlmay be poor because inter-lot shape variation may be large and the fixedwafer bonding recipe does not compensate for inter-lot shape variation.

According to an example embodiment, a wafer bonding system is providedthat utilizes wafer metrology data to tune a wafer bonding recipe. Thewafer metrology data is provided to a model of the wafer bondingprocess, and the model tunes the process conditions of the wafer bondingrecipe to produce post-bond wafers that meet a post-bond distortionthreshold.

While post-bond distortion directly measured on the wafer is important,the cost involved in performing this measurement is high for researchand development purposes. For example, the time to perform all of theaforementioned bonding steps before the metrology step is very long,possibly on the order of weeks. In an embodiment, instead of directpost-bond distortion measurements, physical parameters, such as wafershape data, of the wafer can be measured for the post-bond wafer andused as a proxy to estimate the post-bond distortion. As an example, thewafer shape data is the form of a high density gridded map of thetopology of a freestanding wafer. The grid may have a lateral resolutionof 0.5 mm, for example, and measured over the entire wafer. As anexample, the wafer shape metrology should have a resolution of less than1 nm in an out-of-plane direction, which may be defined as deviationalong a plane orthogonal to the major plane of the wafer. The wafershape data may be directly correlated to the distortion throughgeometric formulations. Examples of geometric formulations include thosethat correlate wafer shape data and distortion based on the theory ofelasticity, beam theory, or free-form wafer shape measurements.

In an embodiment, the wafer metrology data comprises metrology data forpre-bond wafers (e.g., wafer metrology data for the top wafers, thebottom wafers, or both the top and bottom wafers), and the wafermetrology data is provided to the model to tune the process conditionsof the wafer bonding recipe to produce post-bond wafers that meet thepost-bond distortion threshold. In other words, the model is used totune the process conditions of the wafer bonding recipe to producepost-bond wafers that meet the post-bond distortion threshold, given thewafer metrology data of the pre-bond wafers. The model predicts thepost-bond distortion of the post-bond wafers based on the wafermetrology data of the pre-bond wafers and tunes the wafer bonding recipeto produce post-bond wafers that meet the post-bond distortionthreshold. This mode of operation is referred to as feedforwardoperation. A detailed description of feedforward operation is providedbelow.

In an embodiment, the wafer metrology data comprises metrology data forpost-bond wafers, and the wafer metrology data is provided to the modelto tune the process conditions of the wafer bonding recipe to producepost-bond wafers that meet the post-bond distortion threshold. In otherwords, the model is used to tune the process conditions of the waferbonding recipe, based on the metrology data of the post-bond wafers, toproduce post-bond wafers that meet the post-bond distortion threshold.This mode of operation is referred to as feedback operation. A detaileddescription of feedback operation is provided below.

In an embodiment, the wafer metrology data comprises metrology data forboth pre-bond wafers and post-bond wafers, and the metrology data isprovided to the model to tune the process conditions of the waferbonding recipe to product post-bond wafers that meet the post-bonddistortion threshold. Because the metrology data of both the pre-bondwafers and the post-bond wafers is available, the model may be used toboth predict the post-bond distortion of the post-bond wafers based onthe wafer metrology data of the pre-bond wafers and tune the processconditions of the wafer bonding recipe, based on the metrology data ofboth the pre- and post-bond wafers, to produce post-bond wafers thatmeet the post-bond distortion threshold.

FIG. 3 illustrates a wafer bonding system 300 that utilizes wafermetrology data to tune a wafer bonding recipe according to the exampleembodiments presented herein. In an embodiment, wafer bonding system 300uses wafer metrology data of pre-bond wafers (either the top wafer, thebottom wafer, or both the top and bottom wafers) and/or post-bond wafersto tune process conditions of the wafer bonding recipe to producepost-bond wafers that meet a post-bond distortion threshold.

Wafer bonding system 300 includes a wafer processing tool 305 that bondspre-bond wafers (i.e., a top wafer 310 and a bottom wafer 315) toproduce a post-bond wafer 320. Wafer processing tool 305 is widelyavailable commercially. Wafer bonding system 300 includes a shapemetrology tool 325 that measures physical parameters, such as wafershape data, of top wafer 310, bottom wafer 315, or both top wafer 310and bottom wafer 315. An example of shape metrology tool 325 is asurface profilometer. Another example of shape metrology tool 325 is alithography surface scanner, such as atomic force microscopy tools,critical-dimension scanning electron microscopes, and so on. Thephysical parameters may be provided to wafer processing tool 305 to tunethe wafer bonding recipe used to bond the pre-bond wafers so that theresulting post-bond wafer 320 meets post-bond distortion parameters.

As shown in FIG. 3, wafer bonding system 300 is configured to bondpre-bond wafers where top wafer 310 is the patterned wafer and bottomwafer 315 is the carrier wafer, hence it may not be necessary to measurephysical parameters of bottom wafer 315. Therefore, the line betweenbottom wafer 315 and shape metrology tool 325 is shown as a dashed line.However, in a different configuration, physical parameters of bottomwafer 315 may be obtained and used to tune the wafer bonding recipe evenwhen bottom wafer 315 is the carrier wafer. Furthermore, in a situationwhere top wafer 310 is the carrier wafer and bottom wafer 315 is thepatterned wafer, then the physical parameters of bottom wafer 315 wouldbe measured and provided to wafer processing tool 305 to tune the waferbonding recipe, while the measuring of the physical parameters of topwafer 310 may be optional. In the situation where both top wafer 310 andbottom wafer 315 are patterned, the physical parameters of both wafersshould be measured and provided to wafer processing tool 305.

Wafer bonding system 300 also includes a scanner 330 that scanspost-bond wafer 320 to determine distortion data (such as post-bonddistortion data) of post-bond wafer 320. The distortion data may beprovided to wafer processing tool 305 to tune the wafer bonding recipeto help ensure that post-bond wafers meet post-bond distortionparameters. In some embodiments, scanner 330 is also used as shapemetrology tool 325 to measure physical parameters of the pre-bondwafers.

In an embodiment, physical parameters of post-bond wafer 320 areprovided to wafer processing tool 305 to tune the wafer bonding recipeused to bond the pre-bond wafers. Because it is necessary to measurephysical parameters of post-bond wafer 320 in this particular embodimentbut not in all embodiments, the line between post-bond wafer 320 andshape metrology tool 325 is shown as a dashed line.

FIG. 4 illustrates an example wafer processing tool 400 according toexample embodiments presented herein. Wafer processing tool 400 may bean example implementation of wafer processing tool 305 of FIG. 3, forexample.

Wafer processing tool 400 includes a processing chamber 405 where thebonding of the top wafer and the bottom wafer takes place. Processingchamber 405 provides a controlled environment, such as temperature,atmospheric pressure, plasma power, and so forth. Processing chamber 405includes a wafer holder 410. In an embodiment, wafer holder 410 includesa top chuck for holding a top wafer and a bottom chuck for holding abottom wafer. Top wafers 415 and bottom wafers 420 may be transportedinto processing chamber 405 by a top transport mechanism 417 and abottom transport mechanism 422, respectively.

In an embodiment, as the top wafers and bottom wafers are transported toprocessing chamber 405, shape metrology tool 325 measures the wafers,determining physical parameters for the wafers. A processor 425 tunesthe process conditions of the wafer bonding recipe in accordance withthe physical parameters to produce a post-bond wafer that meets thepost-bond distortion threshold. A detailed description of the tuning ofthe process conditions is provided below.

In an embodiment, the top wafers and bottom wafers are pre-measured, byshape metrology tool 325 or some other shape metrology tool, beforebeing loaded into wafer processing tool 400 and the physical parametersfor the wafers are stored in a database stored in a memory 425, forexample. Then, as the top wafers and bottom wafers enter processingchamber 405, a processor 430 retrieves the physical parameters for thewafers and tunes the process conditions of the wafer bonding recipe inaccordance with the physical parameters to produce a post-bond waferthat meets the post-bond distortion threshold. A detailed description ofthe tuning of the process conditions is provided below.

A transport mechanism 437 moves post-bond wafers 435 from processingchamber 405. Scanner 330 scans post-bond wafers 435 to determinedistortion data (such as post-bond distortion data) of the post-bondwafers. The distortion data may also be stored in a database stored inmemory 430.

FIG. 5 illustrates a wafer fusion bonding process 500 with examplefeedback operation according to example embodiments presented herein. Asshown in FIG. 5, a wafer processing tool 505 fusion bonds a first topwafer 510 and a first bottom wafer 515 using a POR wafer bonding recipeto form a first post-bond wafer 520. Distortion data of first post-bondwafer 520 (as measured by a scanner, for example) is provided to waferprocessing tool 505 to optimize the wafer bonding recipe (shown in FIG.5 as “OPT RECIPE”). Subsequent top wafers (e.g., second top wafer 512and N-th top wafer 514) are bonded to subsequent bottom wafers (e.g.,second bottom wafer 517 and N-th bottom wafer 519) in accordance withthe optimized wafer bonding recipe to produce subsequent post-bondwafers (e.g., second post-bond wafer 522 and N-th post-bond wafer 524).In situations where the subsequent wafers are part of the same lot asthe corresponding first wafers, wafer fusion bonding process 500 withfeedback operation may be capable of producing post-bond wafers with awafer bonding recipe optimized for the relatively small variations seenin same lot wafers.

In an embodiment, distortion data of the subsequent post-bond wafers,such as second post-bond wafer 522, may be measured and provided towafer processing tool 505 to further optimize the wafer bonding recipe.As an example, the distortion data of second post-bond wafer 522 ismeasured and provided to wafer processing tool 505 to optimize the waferbonding recipe used to bond a third post-bond wafer. Then, thedistortion data of the third post-bond wafer is measured and provided towafer processing tool 505 to optimize the wafer bonding recipe used tobond a fourth post-bond wafer, and so on.

In an embodiment, in a situation where multiple wafer processing toolsare used to perform wafer fusion bonding, the distortion data of firstpost-bond wafer 520 is provided to the multiple wafer processing toolsto separately optimize the wafer bonding recipe. In another embodiment,in a situation where multiple wafer processing tools are used to performwafer fusion bonding, the optimized wafer bonding recipe is sharedbetween the multiple wafer processing tools.

FIG. 6 illustrates a wafer fusion bonding process 600 with a firstexample feedforward operation according to example embodiments presentedherein. As shown in FIG. 6, a wafer processing tool 605 uses physicalparameters of a first top wafer 610 and a first bottom wafer 615 tooptimize the wafer bonding recipe (shown in FIG. 6 as “OPT RECIPE”).Wafer processing tool 605 uses the optimized wafer bonding recipe tobond first top wafer 610 and first bottom wafer 615 to form firstpost-bond wafer 620. The physical parameters of first top wafer 610 andfirst bottom wafer 615 may be measured before they are used to optimizethe wafer bonding recipe to maximize the time available to optimize thewafer bonding recipe. As an example, the physical parameter measurementsare made as the wafers are received at the wafer fabrication facility(potentially a significant amount of time before they are actuallybonded) and the measurements are stored in a database. As anotherexample, the physical parameter measurements are made as the wafers areloaded into the wafer bonding tool and the measurements are stored in adatabase. In an embodiment, subsequent top wafers (e.g., second topwafer 612 and N-th top wafer 614) are bonded to subsequent bottom wafers(e.g., second bottom wafer 617 and N-th bottom wafer 619) in accordancewith the optimized wafer bonding recipe to produce subsequent post-bondwafers (e.g., second post-bond wafer 622 and N-th post-bond wafer 624).An advantage of wafer fusion bonding process 600 is that (within a waferlot) all of the wafers are bonded using a wafer bonding recipe optimizedfor the relatively small variations seen in same lot wafers.

In an embodiment, in a situation where one of the two pre-bond wafers ispatterned (e.g., the top wafer is patterned and the bottom wafer is acarrier wafer or the bottom wafer is patterned and the top wafer is acarrier wafer), only the physical parameters of the patterned wafer isused to optimize the wafer bonding recipe. As an example, in thesituation when the top wafer is patterned and the bottom wafer is acarrier wafer, wafer processing tool 605 uses the physical parameters offirst top wafer 610 to optimize the wafer bonding recipe used to bondfirst top wafer 610 and first bottom wafer 615. As another example, inthe situation when the bottom wafer is patterned and the top wafer is acarrier wafer, wafer processing tool 605 uses the physical parameters offirst bottom wafer 615 to optimize the wafer bonding recipe used to bondfirst top wafer 610 and first bottom wafer 615.

In an embodiment, in a situation where multiple wafer processing toolsare used to perform wafer fusion bonding, the physical parameters offirst top wafer 610 and first bottom wafer 615 are provided to themultiple wafer processing tools to separately optimize the wafer bondingrecipe. In another embodiment, in a situation where multiple waferprocessing tools are used to perform wafer fusion bonding, the optimizedwafer bonding recipe is shared between the multiple wafer processingtools.

FIG. 7 illustrates a wafer fusion bonding process 700 with a secondexample feedforward operation according to example embodiments presentedherein. As shown in FIG. 7, a wafer processing tool 705 uses physicalparameters of a first top wafer 710 and a first bottom wafer 715 tooptimize the wafer bonding recipe (shown in FIG. 7 as “OPT RECIPE 1”).Wafer processing tool 705 uses the optimized wafer bonding recipe tobond first top wafer 710 and first bottom wafer 715 to form firstpost-bond wafer 720. The physical parameters of first top wafer 710 andfirst bottom wafer 715 may be measured before they are used to optimizethe wafer bonding recipe to maximize the time available to optimize thewafer bonding recipe. As an example, the physical parameter measurementsare made as the wafers are received at the wafer fabrication facility(potentially a significant amount of time before they are actuallybonded) and the measurements are stored in a database. As anotherexample, the physical parameter measurements are made as the wafers areloaded into the wafer bonding tool and the measurements are stored in adatabase. Physical parameters of subsequent top wafers and subsequentbottom wafers are used to optimize wafer bonding recipes used to bondsubsequent post-bond wafers. As an example, physical parameters ofsecond top wafer 712 and second bottom wafer 717 are used to optimize awafer bonding recipe used to bond second post-bond wafer 722. Similarly,physical parameters of N-th top wafer 714 and N-th bottom wafer 719 areused to optimize a wafer bonding recipe used to bond N-th post-bondwafer 724. An advantage of wafer fusion bonding process 700 is that thewafer bonding recipe is individually optimized for the wafers beingbonded, independent of wafer lot.

In an embodiment, in a situation where one of the two pre-bond wafers ispatterned (e.g., the top wafer is patterned and the bottom wafer is acarrier wafer), only the physical parameters of the patterned wafer isused to optimize the wafer bonding recipe. As an example, waferprocessing tool 705 uses the physical parameters of first top wafer 710to optimize the wafer bonding recipe used to bond first top wafer 710and first bottom wafer 715.

In an embodiment, a combination of feedforward and feedback optimizationof the wafer bonding recipe is used. In such an embodiment, both thephysical parameters of the pre-bond wafers and the distortion data ofpost-bond wafers are used to optimize the wafer bonding recipes.

FIG. 8 illustrates a flow diagram of example operations 800 occurring ina wafer bonding process with feedforward optimization of the waferbonding recipe according to example embodiments presented herein.Operations 800 may be indicative of operations occurring in a waferprocessing tool as the wafer processing tool bonds wafers using a waferbonding process with feedforward optimization of the wafer bondingrecipe. Operations 800 may be descriptive of wafer fusion bondingprocesses 600 and 700.

Operations 800 begin with the creating of a model of the wafer bondingprocess (block 805). The model of the wafer bonding process is amathematical model of the wafer bonding process that takes into accountphysical parameters of the pre-bond wafers, as well as the processconditions of the wafer bonding process and relates them to simulateddistortion of the post-bond wafers. The model of the wafer bondingprocess may utilize any combination of finite element analysis, linearregression, random forest algorithm, genetic programming algorithm,patterned search algorithm, neural network algorithm, deep learningalgorithm, and so on, to relate the simulated distortion of thepost-bond wafers to the physical parameters of the pre-bond wafers andthe process conditions of the wafer bonding process. In an embodiment,the model is created a priori by a wafer processing tool and stored in amemory. In an embodiment, the model is created by a tool not directlyinvolved in the bonding of wafers and then provided to the waferprocessing tool. A detailed discussion of several approaches that may beused to create the model is provided below.

The process conditions of the wafer bonding process may includepre-clean time, pre-clean solution chemical composition, temperature ofthe top wafer chuck, temperature of the bottom wafer chuck, plasmapre-treatment time, plasma pre-treatment power, wafer idle time, bondingtime, bond initiation force, chamber atmospheric pressure, chamber meantemperature, chamber humidity, chamber atmospheric gas composition,bottom wafer chuck vacuum pressures, bottom wafer chuck vacuum zones,bottom wafer chuck vacuum on/off time, top wafer chuck vacuum pressures,top wafer chuck vacuum zones, top wafer chuck vacuum on/off time, topwafer chuck height variation, bottom wafer chuck height variation,bonding gap, pre-chuck wafer temperature, chuck height, etc. The modelof the wafer bonding process may consider any or all of the processconditions listed above.

The wafer processing tool measures an incoming top wafer (block 807).The wafer processing tool may measure the top wafer using any of avariety of metrology tools, such as a surface profilometer, and so on,or a scanner. The measurement of the top wafer provides physicalparameters (such as wafer shape data) of the top wafer. The waferprocessing tool optionally measures an incoming bottom wafer (block809). The measuring of the incoming bottom wafer may follow a processsimilar to the measuring of the top wafer described above. In asituation where the incoming bottom wafer is a carrier wafer (i.e., thebottom wafer is unpatterned), the impact of the physical parameters ofthe bottom wafer on the distortion of the post-bond wafer may beinsignificant compared to the impact of the patterned wafer (e.g., theincoming top wafer). Hence, it may be unnecessary to measure the bottomwafer. However, if both wafers are patterned, then the incoming bottomwafer should be measured. The distortions of the incoming wafers arealso obtained from the measurements of the incoming wafers (block 811).The distortions of the wafers may be obtained using the same techniqueused during the creation of the model of the wafer bonding process, forexample. Fingerprint coefficients may also be obtained for the wafers,using the same technique described in detail below for creating themodel of the wafer bonding process, for example.

The wafer bonding recipe for bonding the incoming top wafer and theincoming bottom wafer is generated (block 813). The wafer bonding recipeis generated in accordance with the physical parameters of the incomingtop wafer and the incoming bottom wafer (optional) and a post-bonddistortion threshold for the post-bond wafer. The wafer bonding recipeis generated using the model of the wafer bonding process, as created inblock 805, for example. In an embodiment, the wafer bonding recipe isgenerated by optimizing the wafer bonding recipe based on the physicalparameters of the incoming top wafer and the incoming bottom wafer(optional), and the process conditions of an initial wafer bondingrecipe (e.g., the POR wafer bonding recipe for the wafer bondingprocess). As an example, the wafer bonding recipe is optimized by tuningthe process conditions until the estimated post-bond distortion isreduced to the point where the estimated post-bond distortion is lessthan a post-bond distortion threshold. The optimization algorithm may bea linear programming algorithm, a genetic algorithm, random forestalgorithm, a regression algorithm, or other techniques.

The optimized wafer bonding recipe comprising the optimized processconditions are the instructions for the wafer processing tool to performthe wafer bonding process for the incoming pre-bond wafer pair. Theoptimized process conditions may include pre-clean time, pre-cleansolution chemical composition, temperature of the top wafer chuck,temperature of the bottom wafer chuck, plasma pre-treatment time, plasmapre-treatment power, wafer idle time, bonding time, bond initiationforce, chamber atmospheric pressure, chamber mean temperature, chamberhumidity, chamber atmospheric gas composition, bottom wafer chuck vacuumpressures, bottom wafer chuck vacuum zones, bottom wafer chuck vacuumon/off time, top wafer chuck vacuum pressures, top wafer chuck vacuumzones, top wafer chuck vacuum on/off time, top wafer chuck heightvariation, bottom wafer chuck height variation, bonding gap, pre-chuckwafer temperature, chuck height, etc.

In an embodiment, the model of the wafer bonding process, with thephysical parameters of the incoming top wafer and the incoming bottomwafer (optional) and an initial wafer bonding recipe (e.g., the PORwafer bonding recipe), is used to estimate the post-bond distortion ofthe post-bond wafer. If the estimated post-bond distortion meets thepost-bond distortion threshold, the initial wafer bonding recipe isselected as the wafer bonding recipe for bonding the incoming top waferand the incoming bonding wafer. If the estimated post-bond distortiondoes not meet the post-bond distortion threshold, the wafer processingtool changes one or more process conditions of the wafer bonding recipeand re-estimates the post-bond distortion. In an iterative process,wafer processing tool may continue to change process conditions untilthe estimated post-bond distortion meets the post-bond distortionthreshold (or until a specified number of iterations is met, forexample).

In an embodiment, a first wafer bonding recipe that results in anestimated post-bond distortion meeting the post-bond distortionthreshold is the wafer bonding recipe used to bond the incoming wafers.In an embodiment, in a situation where a plurality of wafer bondingrecipes result in estimated post-bond distortions that meet thepost-bond distortion threshold, then the wafer bonding recipe out of theplurality of wafer bonding recipes with a smallest number of processcondition changes (or a smallest amount of change to the processconditions, a process condition change easiest to implement, etc.) isthe wafer bonding recipe used to bond the incoming wafers. As anexample, if the estimated post-bond distortion for two wafer bondingrecipes are about equal, then the wafer bonding recipe with changes tothe process conditions that are the easiest to implement (e.g., smallestnumber of changes, smallest amount of changes, changes to more readilychangeable process conditions, and so on) is the wafer bonding recipeused to bond the incoming wafers.

The wafer processing tool bonds the incoming top wafer and the incomingbottom wafer (block 815). The wafer processing tool bonds the incomingwafers using the wafer bonding recipe generated in block 813, forexample.

A check is performed to determine if there are more wafers to bond(block 819). If there are additional wafers to bond, then another checkis performed to determine if the additional wafers are from the samewafer lot as the wafers previously bonded (block 821). The same lotcomparison may be performed by comparing the wafer lot associated withthe wafer bonding recipe generated in block 813 with the wafer lot of anincoming wafer pair, for example. This check may be useful indetermining if the wafer bonding recipe should be re-optimized. Asdiscussed previously, intra-lot variation is significantly smaller thaninter-lot variation. If the additional wafers are from the same waferlot, then it may not be necessary to re-optimize the wafer bondingrecipe. If there are no additional wafers to bond, the wafer processingtool may stop the wafer bonding process and potentially performadditional processing on the post-bond wafers (block 829). Additionalprocessing of the post-bond wafers may include annealing the post-bondwafers, which involves holding the post-bond wafers at an elevatedtemperature for a specified amount of time to strengthen the bondbetween the wafers of the post-bond wafers.

If the additional wafers are from the same wafer lot, then the samewafer bonding recipe generated in block 813 may be used to bond theadditional wafers. The wafer processing tool returns to block 815 bondanother pair of incoming wafers (shown as dot-dashed line 823). Thewafer processing tool uses the same wafer bonding recipe to bond thispair of incoming wafers. The operations illustrated when the waferprocessing tool returns to block 815 to bond another pair of incomingwafers without making measurements of the pair of incoming wafers orpotentially updating the wafer bonding recipe is an example of the waferfusion bonding process illustrated in FIG. 6.

Although re-optimization of the wafer bonding recipe may not be requiredfor same lot wafers, it is still possible to re-optimize the waferbonding recipe for each incoming wafer pair. In such a situation, thewafer processing tool returns to block 807 to measure the incomingwafers, obtain distortions, generate another wafer bonding recipe, andbond the incoming wafers (shown as double-dot-dashed line 825). Theoperations illustrated when the wafer processing tool returns to block807 to bond another pair of incoming wafers with making measurements ofthe pair of incoming wafers and potentially updating the wafer bondingrecipe is an example of the wafer fusion bonding process illustrated inFIG. 7. As one practical implementation, the wafer measurements may beperformed for each wafer pair before the wafers enters the waferprocessing tool, for example, when or before undergoing prior steps.This may provide additional time for generating the optimized processrecipe. In such cases, the model may include corrections to offsetadditional distortions to be introduced by the to be performedprocessing steps.

If the additional wafers are not from the same wafer lot, then the samewafer bonding recipe generated in block 813 may not be able to bondwafers with post-bond distortions that meet the post-bond distortionthreshold. The wafer bonding recipe should be regenerated with thephysical parameters of the additional wafers. The wafer processing toolreturns to block 807 to measure the incoming wafers, obtain distortions,generate another wafer bonding recipe, and bond the incoming wafers(shown as solid line 827).

In an embodiment, measurements of the post-bond wafers that are bondedutilizing the optimized wafer bonding recipe are used to refine themodel of the wafer bonding process to help improve the estimationaccuracy of the model. The refinement of the model may follow thetechniques illustrated in FIG. 9 or 10 or some other technique.

FIG. 9A illustrates a flow diagram of first example process 900 increating a model of a wafer bonding process using a finite elementmodeling (FEM) technique enhanced with semi-empirical modeling accordingto example embodiments presented herein. Process 900 may be indicativeof operations occurring in a tool, such as a wafer processing tool or atool dedicated to model creation, as the tool creates the model of thewafer bonding process using FEM techniques enhanced with semi-empiricalmodeling. In an embodiment, the FEM techniques are used to simulate thewafer bonding process over a wide range of incoming wafer physicalcharacteristics and wafer bonding recipe process conditions. Forexample, wafers with different shape profiles may be simulated to obtaincertain patterns and/or behavior that can then be represented in themodel of the wafer bonding process. The FEM simulation typically solvesa stress-strain equation using a 2-D (two dimensional) or 3-D (threedimensional) representation of the wafer using a finite element analysistechnique. As one practical implementation, the wafer measurements maybe performed for each wafer pair before the wafers enter the waferprocessing tool, for example, when or before undergoing prior steps.This may provide additional time for generating the optimized processrecipe. In such cases, the model may include corrections to offsetadditional distortions to be introduced by the to be performedprocessing steps. In addition, the FEM simulation in parallel may alsosimulate other physical processes that change the stress-strain of thesystem during the wafer bonding process. Some examples include reactionswithin the wafer, reflow of materials that change the stress, outgassingfrom different material layers within the wafer, and so forth.

In an embodiment, semi-empirical modeling is utilized to create andrefine a model using data generated using the FEM techniques. Process900 may be an example implementation of block 805 of FIG. 8.

Process 900 begins with physical characteristics of pre-bond wafers andprocess conditions of a wafer bonding recipe (block 905). The physicalcharacteristics of the pre-bond wafers and the process conditions of thewafer bonding recipe may be inputs to a simulation of the wafer bondingprocess. The input is provided to a FEM simulation of the physicsinvolved in the wafer bonding process (block 907). The FEM simulationproduces simulated post-bond wafers based on the input (i.e., thephysical characteristics of the pre-bond wafers and the processconditions of the wafer bonding recipe such as applied pressure andtemperature, etc.). The FEM simulation produces simulated post-bondwafers (block 907). The simulated distortion data of the simulatedpost-bond wafers may be determined.

FEM simulation is able to simulate the output (i.e., the distortion dataof the post-bond wafers) from the input (i.e., the physicalcharacteristics of the pre-bond wafers and the process conditions of thewafer bonding recipe such as applied pressure and temperature) using thephysics involved in wafer bonding, FEM simulation may be computationallyintensive. Therefore, while FEM simulation may be directly used inactual wafer bonding deployments, certain embodiments may use asemi-empirical model. In an embodiment, semi-empirical modeling is usedto help reduce the computational requirements associated with thecreation of the model of the wafer bonding process.

The input and the output may be provided to a semi-empirical modelingprocess (block 911). The inputs may be for wide range of pre-bond wafersand wafer lots, as well as a range of process conditions or waferbonding recipes, while the outputs comprise simulated distortion data ofsimulated post-bond wafers bonded in accordance with the inputs. Thesemi-empirical modeling process is used to determine relationshipsbetween the inputs and the outputs. In other words, the semi-empiricalmodeling process utilizes empirical data (the inputs and the outputs) todevelop a model of the wafer bonding process. The semi-empiricalmodeling process outputs the model of the wafer bonding process. Thesemi-empirical modeling process may use any of a variety of techniques,such as optimization techniques, searching techniques, annealingtechniques, machine learning techniques (including but not limited toneural network techniques, deep learning techniques, regressiontechniques, classification techniques, clustering techniques,dimensionality reduction techniques, ensemble methods, transfer learningtechniques, reinforcement learning techniques, and so on), etc.

In an embodiment, actual data (e.g., actual physical characteristics ofpre-bond wafers, process conditions of wafer bonding recipes, and actualdistortion of post-bond wafers bonded using the wafer bonding recipes)is also provided to the semi-empirical modeling process to help furtherrefine the model of the wafer bonding process.

FIG. 9B illustrates a flow diagram of second example process 950 increating a model of a wafer bonding process using FEM techniquesenhanced with semi-empirical modeling and calibration according toexample embodiments presented herein. Calibration may help to refine themodel to account for parameters that may be difficult to account in aFEM simulation, for example, a pre-clean time or time over which thewafer are held together in block 815 prior to annealing or the annealtime. In addition, many of the process knobs may be modeled similarly,e.g., top wafer chuck position and bottom wafer chuck position may beindependently controlled but may be modelled similarly as a physicalmodel only uses the relative distances. Therefore, a calibration may benecessary to tie the various process conditions with the model.

Further, while the calibration is described below with respect to thesemi-empirical modeling, a similar approach may be used if the FEMsimulation is directly used without the semi-empirical model.

Process 950 may be indicative of operations occurring in a tool, such asa wafer processing tool or a tool dedicated to model creation, as thetool creates the model of the wafer bonding process using FEM techniquesenhanced with semi-empirical modeling. In addition, calibration may beused to determine the contribution and impact of different processparameters on the model of the wafer bonding process. Process 950 may bean example implementation of block 805 of FIG. 8.

As shown in FIG. 9B, a model of the wafer bonding process is createdusing FEM techniques enhanced with semi-empirical modeling (block 955).The model may be created based on physical characteristics of pre-bondwafers and process conditions of wafer bonding recipes. Process 900 ofFIG. 9A may be an example of block 955.

After the model has been created, or as the model is being created,calibration may be performed (block 957). Calibration may involvesetting one or more process conditions of the wafer bonding recipe topredetermined values and then simulating the outputs in accordance tothe calibrated process conditions. Calibration may be used to determinethe impact or contribution of individual process conditions on thedistortion of the post-bond wafer. As an example, one process conditionis changed with the remainder of the process conditions held constant,and the resulting post-bond wafer is simulated using the processconditions. The model of the wafer bonding process may be refined inaccordance with the simulated results.

FIG. 10 illustrates a flow diagram of example operations 1000 occurringin creating a model of a wafer bonding process using fingerprintingfunctions according to example embodiments presented herein. Operations1000 may be indicative of operations occurring in a tool, such as awafer processing tool or a tool dedicated to model creation, as the toolcreates the model of the wafer bonding process using fingerprintingfunctions. Operations 1000 may be an example implementation of block 805of FIG. 8.

Operations 1000 begin with the measuring of wafer to obtain physicalparameters (block 1005). The wafers measured include pre-bond wafers(i.e., the top wafers and the bottom wafers). The wafers may be measuredusing a metrology tool (such as a surface profilometer, a scanner, andso on). In a situation where both the top and bottom wafers arepatterned, both wafers should be measured. In a situation where only onewafer is patterned, it may not be necessary to measure the unpatternedwafer. Measurements are made for a plurality of pre-bond wafers. As anexample, top and bottom wafers from different lots are measured.Distortions are obtained from the measurements of the wafers (block1007). The distortions may be derived from the measurements of thewafers through geometric calculations, for example.

Fingerprinting functions are fitted to the distortions (block 1009). Ingeneral, fingerprinting functions are mathematical models of arespective metric that retains spatial information of the measurements.The fitting of the fingerprinting functions to the distortions mayinvolve an analysis (such as a regression analysis) to select and adjustthe parameters of the fingerprinting functions to fit the distortionswithin a specified threshold. As an example, the distortions may beexpressed mathematically as a finite series of fingerprinting functions,such as polynomials (e.g., Zernike polynomials) or functions (such as,Fourier series or Bessel function). Each polynomial or function isweighed by a respective coefficient that are the fitting parameters thatmay be adjust to fit the distortions. The fitted coefficients representunique tendencies of the distortion for a particular wafer. In anembodiment, the fingerprinting functions are fitted to the distortionsof the wafers (e.g., the pre-bond wafers and the post-bond wafers).

The fitted coefficients of the fingerprinting functions are fitted asfunction of process conditions of the wafer bonding process (block1011). The model of the wafer bonding process may be created by fittingthe fitted coefficients of the fingerprinting functions as a function ofthe process conditions of the wafer bonding process used to bond thewafers, as well as the distortion of the pre-bond wafers (which are alsoconverted into fingerprinting coefficients). The function may be linear,quadratic, exponential, or other nonlinear forms, with one or more ofthe process conditions discussed previously. The function may alsoinclude terms where two or more of the process conditions interact witheach other in linear, quadratic, exponential, or other nonlinear form,ways. The exact format of the function may be determined through acomputer algorithm, for example linear regression, random forest,genetic programming, pattern search, neural network, deep learning, etc.The model is constructed so that the post-bond wafer distortion can beestimated by using any process conditions that is within the limits ofthe database, and the pre-bond wafer physical parameters. The ability ofthe model of the wafer bonding process to estimate the distortion of thepost-bond wafer in accordance with the process conditions of the waferbonding process and the characteristics of the pre-bond wafers enablesthe optimization of the wafer bonding recipe before the bonding of thepre-bond wafer pair. A detailed description of fingerprinting functionsand examples of their use is provided in co-assigned U.S. patentapplication Ser. No. 16/666,087, entitled “Systems and Methods forManufacturing Microelectronic Devices,” filed Oct. 28, 2019, which ishereby incorporated by reference in its entirety.

FIG. 11 illustrates a flow diagram of operations 1100 occurring in ageneration of a wafer bonding recipe with feedforward based optimizationof the wafer bonding recipe according to example embodiments presentedherein. Operations 1100 may be indicative of operations occurring in awafer processing tool as the wafer processing tool generates a waferbonding recipe with feedforward based optimization of the wafer bondingrecipe. Operations 1100 may be an example implementation of block 813 ofFIG. 8.

Operations 1100 begin with the model in block 805 of the waferprocessing tool estimating the post-bond distortion of a post-bond waferthat is bonded using an initial wafer bonding recipe (block 1105). Thepost-bond distortion of the post-bond wafer is estimated using thephysical parameters of the incoming wafers and the process conditionsfor the initial wafer bonding recipe (e.g., the POR wafer bondingrecipe) as described in blocks 807, 809, 811 of FIG. 8.

A check is performed to determine if the estimated post-bond distortionof the post-bond wafer meets the post-bond distortion threshold (block1107). If the estimated post-bond distortion meets the post-bonddistortion threshold, then the wafer processing tool creates the waferbonding recipe (block 1109). The wafer bonding recipe is created fromthe process condition of wafer bonding recipe used in the estimation ofblock 1105. The wafer bonding recipe includes any changes to the processconditions of the initial wafer bonding recipe.

If the estimated post-bond distortion of the post-bond wafer does notmeet the post-bond distortion threshold, a change is made to one or moreprocess conditions of the wafer bonding recipe (block 1111). A selectionof the one or more process conditions may be based on factors such ashow far the estimated post-bond distortion is away from meeting thepost-bond distortion threshold, impact of a process condition on thepost-bond distortion, ease of changing a process condition, and soforth. As an example, if the estimated post-bond distortion is close tomeeting the post-bond distortion threshold, the one or more processconditions changed may be process conditions that are easy to change andhave been shown to have change the estimated post-bond distortion in afine grained manner, while if the estimated post-bond distortion is farfrom meeting the post-bond distortion threshold, the one or more processconditions changed may process conditions that have been shown to havecoarse grained changes to the estimated post-bond distortion (topotentially reduce a number of times the wafer bonding recipe is changedbefore the estimated post-bond distortion meets the post-bond distortionthreshold, for example). As another example, some process conditions areeasier to change. For example, wafer chuck temperature may be easier tochange than pre-clean solution chemical composition. Therefore, suchprocess conditions are more feasible candidates for changing.

Example changes to the wafer bonding recipe may include changes to anyone or more of the following process conditions: pre-clean time,pre-clean solution chemical composition, temperature of the top waferchuck, temperature of the bottom wafer chuck, plasma pre-treatment time,plasma pre-treatment power, wafer idle time, bonding time, bondinitiation force, chamber atmospheric pressure, chamber meantemperature, chamber humidity, chamber atmospheric gas composition,bottom wafer chuck vacuum pressures, bottom wafer chuck vacuum zones,bottom wafer chuck vacuum on/off time, top wafer chuck vacuum pressures,top wafer chuck vacuum zones, top wafer chuck vacuum on/off time, topwafer chuck height variation, bottom wafer chuck height variation,bonding gap, pre-chuck wafer temperature, chuck height, and so forth.The wafer processing tool returns to block 1105 to re-estimate thepost-bond distortion of the post-bond wafer bonded in accordance withthe changed wafer bonding recipe.

FIG. 12 illustrates a flow diagram of example operations 1200 occurringin a wafer bonding process with feedback optimization of the waferboding recipe according to example embodiments presented herein.Operations 1200 may be indicative of operations occurring in a waferprocessing tool as the wafer processing tool bonds wafers using a waferbonding process with feedback optimization of the wafer bonding recipe.Operations 1200 may be descriptive of wafer fusion bonding process 500.

Operations 1200 begin with the creating of a model of the wafer bondingprocess (block 1205). The model of the wafer bonding process may becreated based on measurements of post-bond wafers and process conditionsof the wafer bonding recipes. The model of the wafer bonding process mayutilize any combination of finite element analysis, linear regression,random forest algorithm, genetic programming algorithm, patterned searchalgorithm, neural network algorithm, deep learning algorithm, and so on,to relate the distortion of the post-bond wafers to the processconditions of the wafer bonding recipes. In an embodiment, the model iscreated a priori by a wafer processing tool and stored in a memory. Inan embodiment, the model is created by a tool not directly involved inthe bonding of wafers and then provided to the wafer processing tool. Adetailed discussion of an example approach that may be used to createthe model is provided below.

The wafer processing tool bonds the incoming top wafer and the incomingbottom wafer (block 1207). In an embodiment, the wafer processing toolbonds the incoming top and bottom wafers using a POR wafer bondingrecipe, for example. In another embodiment, the wafer processing toolbonds the incoming top and bottom wafers using a most recent waferbonding recipe used for bonding wafers of the same design. The wafersbeing bonded may be from the same lot or from different lots of wafersbonded using the most recent wafer bonding recipe. The wafer processingtool measures the post-bond wafer (block 1209). As an example,distortions of the post-bond wafer are measured. The measurement of thepost-bond wafer may be performed using a metrology tool, such as ascanner.

The wafer bonding recipe is generated (block 1215). The wafer bondingrecipe is generated in accordance with the measurements (e.g.,distortions) of the post-bond wafer. The wafer bonding recipe isgenerated using the model of the wafer bonding process, as created inblock 1205, for example. In an embodiment, the wafer bonding recipe isgenerated by optimizing the wafer bonding recipe based on thedistortions of the post-bond wafer, and the process conditions of aninitial wafer bonding recipe (e.g., the POR wafer bonding recipe for thewafer bonding process). As an example, the wafer bonding recipe isoptimized by tuning the process conditions until the estimated post-bonddistortion is reduced to the point where the estimated post-bonddistortion is less than a post-bond distortion threshold. Theoptimization algorithm may be a linear programming algorithm, a geneticalgorithm, random forest algorithm, a regression algorithm, or othertechniques.

The wafer processing tool bonds an incoming wafer pair (block 1217). Theincoming wafer pair is bonded in accordance with the wafer bondingrecipe generated in block 1215.

A check is performed to determine if there are more wafers to bond(block 1221). If there are additional wafers to bond, then another checkis performed to determine if the additional wafers are from the samewafer lot as the wafers previously bonded (block 1223). The same lotcomparison may be performed by comparing the wafer lot associated withthe wafer bonding recipe generated in block 1215 with the wafer lot ofan incoming wafer pair, for example. This check may be useful indetermining if the wafer bonding recipe should be re-optimized. Asdiscussed previously, intra-lot variation is significantly smaller thaninter-lot variation.

If the additional wafers are from the same wafer lot, then it may not benecessary to re-optimize the wafer bonding recipe. In such a situation,the wafer processing tool returns to block 1217 to bond an incomingwafer pair (shown as line 1225). In an embodiment, even if theadditional wafers are from the same wafer lot, the wafer bonding recipemay still be re-optimized. In this situation, the wafer processing toolreturns to block 1209 to make measurements of the post-bond wafer anduses the measurements to generate the wafer bonding recipe in block1215. If there are no additional wafers to bond, the wafer processingtool may stop the wafer bonding process and potentially performadditional processing on the post-bond wafers (block 1229). Additionalprocessing of the post-bond wafers may include annealing the post-bondwafers, which involves holding the post-bond wafers at an elevatedtemperature for a specified amount of time to strengthen the bondbetween the wafers of the post-bond wafers.

If the additional wafers are from a different wafer lot, the waferprocessing tool returns to block 1207 (shown as line 1227) to bond aninitial pair of wafers from the different wafer lot. The post-bond wafercomprising the initial pair of wafers from the different wafer lot maybe used to generate a new wafer bonding recipe usable in bondingremaining wafers from the different wafer lot.

In an embodiment, measurements of the post-bond wafers that are bondedutilizing the optimized wafer bonding recipe are used to refine themodel of the wafer bonding process to help improve the estimationaccuracy of the model.

FIG. 13 illustrates a flow diagram of an example process 1300 increating a model of a wafer bonding processing using feedback dataaccording to example embodiments presented herein. Process 1300 may beindicative of operations occurring in a tool, such as a wafer processingtool or a tool dedicated to model creation, as the tool creates themodel of the wafer bonding process using feedback data using feedbackdata. In an embodiment, fingerprinting functions are used in determininga relationship between distortion data of post-bond wafers and processconditions of wafer bonding recipes. The use of fingerprintingadvantageously reduces the computational load to generate the optimizedprocess recipe for each wafer. Process 1300 may be an exampleimplementation of block 1205 of FIG. 12.

Process 1300 begins with inputs, such as measurements of post-bondwafers (block 1305) and process conditions of wafer bonding recipes(block 1307) being provided to a fingerprinting process (block 1309).The measurements, such as distortion measurements, of the post-bondwafers may be taken for post-bond wafers after they have been bonded(using a particular wafer bonding recipe) or after they have beenannealed. The process conditions correspond to the wafer bonding recipesused to form the post-bond wafers. The fingerprinting process mayinclude the fitting of fingerprinting functions to the distortionmeasurements. As discussed previously, fingerprinting functions aremathematical models of a respective metric that retains spatialinformation of the measurements. The fitted coefficients representunique tendencies of the distortion for a particular post-bonded wafer.In an embodiment, the fingerprinting functions are fitted to thedistortions of the post-bond wafers.

The fitted coefficients of the fingerprinting functions are provided toa modeling process to create the model of the wafer bonding process(block 1311). The fitted coefficients of the fingerprinting functionsmay be fitted as function of process conditions of the wafer bondingprocess. The model of the wafer bonding process may be created byfitting the fitted coefficients of the fingerprinting functions as afunction of the process conditions of the wafer bonding process used tobond the wafers, as well as the distortion of the post-bond wafers. Thefunction may be linear, quadratic, exponential, or other nonlinearforms, with one or more of the process conditions discussed previously.The function may also include terms where two or more of the processconditions interact with each other in linear, quadratic, exponential,or other nonlinear form, ways. The exact format of the function may bedetermined through a computer algorithm, for example linear regression,random forest, genetic programming, pattern search, neural network, deeplearning, etc. The model is constructed so that the process conditionsmay be estimated by using any distortion of the post-bond wafers. Theability of the model of the wafer bonding process to estimate theprocess conditions in accordance with the distortion of the post-bondwafers enables the optimization of the wafer bonding recipe from thedistortion measurements of the post-bond wafers.

FIG. 14 illustrates a flow diagram of operations 1400 occurring in ageneration of a wafer bonding recipe with feedback based optimization ofthe wafer bonding recipe according to example embodiments presentedherein. Operations 1400 may be indicative of operations occurring in awafer processing tool as the wafer processing tool generates a waferbonding recipe with feedback based optimization of the wafer bondingrecipe. Operations 1400 may be an implementation of block 1215 of FIG.12.

Operations 1400 begin with the wafer processing tool identifying aportion of a post-bond wafer that fails to meet a post-bond waferdistortion threshold (block 1405). As an example, given a particularpost-bond wafer, with distortion that fails to meet a post-bond waferdistortion threshold, the wafer processing tool determines one or moreportions of the post-bond wafer with distortion measurements that failto meet the post-bond wafer distortion threshold. In an embodiment, in asituation where there is a plurality of portions of the post-bond waferthat all fail the post-bond wafer distortion threshold, the waferprocessing tool may identify a subset of the plurality, correct for thatidentified subset, and repeat for other subsets of the plurality untilall of the plurality of portions of the post-bond wafer that all failthe post-bond wafer distortion threshold have been addressed.

The wafer processing tool makes a change to the distortion of theidentified portion of the post-bond wafer (block 1407). In anembodiment, the wafer processing tool changes the distortion in only theidentified portion of the post-bond wafer. In an embodiment, the waferprocessing tool changes the distortion in not just the identifiedportion of the post-bond wafer, but in adjacent and, perhaps, nearbyportions of the post-bond wafer, to make the change in the distortioncontinuous. The definition of “nearby portions” may be a configurationparameter for the wafer processing tool, potentially trading offoptimization complexity for bonding performance.

The wafer processing tool estimates the process conditions of the waferbonding recipe that would produce a post-bond wafer with the distortionmeasurements (block 1409). In other words, the wafer processing tool,using the model of the wafer bonding process, estimates the processconditions that would result in a post-bond wafer that would have ameasured distortion that matches the changed distortion. The waferprocessing tool creates the wafer bonding recipe from the estimatedprocess conditions (block 1411).

Example 1. A method including: having a first wafer bonding recipe and amodel of a wafer bonding process, the model including an inputindicative of a physical parameter of a first wafer to be bonded to asecond wafer and configured to output a wafer bonding recipe based, atleast in part, on the physical parameter of the first wafer; obtainingmeasurements of the first wafer to obtain the physical parameter of thefirst wafer; generating, by the model, the first wafer bonding recipebased, at least in part, on the physical parameter of the first wafer;and bonding the first wafer to the second wafer in accordance with thefirst wafer bonding recipe to produce a first post-bond wafer.

Example 2. The method of example 1, further including annealing thefirst post-bond wafer to produce a fusion bonded wafer.

Example 3. The method of one of examples 1 or 2, where the physicalparameter of the first wafer includes out-of-plane distortions of thefirst wafer.

Example 4. The method of one of examples 1 to 3, further includingderiving the model of the wafer bonding process including: obtainingmeasurements of third wafer and fourth wafer to obtain a physicalparameter of the third wafer and a physical parameter of the fourthwafer; simulating wafer bonding of the third wafers and the fourthwafers in accordance with process conditions to estimate a physicalparameter of a simulated post-bond wafer; and creating the model of thewafer bonding process in accordance with the physical parameter of thethird wafer, the physical parameter of the fourth wafer, the processconditions, and the estimated physical parameter of the simulatedpost-bond wafer.

Example 5. The method of one of examples 1 to 4, where creating themodel of the wafer bonding process includes: comparing the physicalparameter of the third wafer, the physical parameter of the fourthwafer, the process conditions, and the estimated physical parameter ofthe simulated post-bond wafer; and determining the model of the waferbonding process in accordance with the comparison.

Example 6. The method of one of examples 1 to 5, where generating thefirst wafer bonding recipe includes: estimating, by the model, post-bonddistortions of the first post-bond wafer in accordance with the physicalparameter of the first wafer; tuning process conditions of the firstwafer bonding recipe to optimize the estimated post-bond distortions ofthe first post-bond wafer; and generating the first wafer bonding recipein accordance with the tuned process conditions.

Example 7. The method of one of examples 1 to 6, further includingobtaining measurements of the second wafer to obtain a physicalparameter of the second wafer, where the first wafer bonding recipe isfurther generated based on the physical parameter of the second wafer.

Example 8. The method of one of examples 1 to 7, further includingbonding a third wafer to a fourth wafer in accordance with the firstwafer bonding recipe to produce a second post-bond wafer.

Example 9. The method of one of examples 1 to 8, where the first waferand the third wafer are part of a first wafer lot, and the second waferand the fourth wafer are part of a second wafer lot, and where the firstwafer lot and the second wafer lot are processed together in asemiconductor fabrication process flow.

Example 10. A method including: having a model of a wafer bondingprocess, the model including an input indicative of a physical parameterof a first post-bond wafer and configured to output a wafer bondingrecipe based on the physical parameter of the first post-bond wafer;bonding a first wafer to a second wafer in accordance with a waferbonding recipe, to form the first post-bond wafer; obtainingmeasurements of the first post-bond wafer to obtain the physicalparameter of the first post-bond wafer; generating, by the model, thefirst wafer bonding recipe in accordance with the physical parameter ofthe first post-bond wafer; and bonding a third wafer to a fourth waferin accordance with the first wafer bonding recipe, to form a secondpost-bond wafer.

Example 11. The method of example 10, further including annealing thefirst post-bond wafer to produce a first fusion bonded wafer; andannealing the second post-bond wafer to produce a second fusion bondedwafer.

Example 12. The method of one of examples 10 or 11, where obtainingmeasurements includes scanning the first post-bond wafer to obtain thephysical parameter of the first post-bond wafer.

Example 13. The method of one of examples 10 to 12, further including:obtaining measurements of the second post-bond wafer to obtain aphysical parameter of the second post-bond wafer; generating, by themodel, a second wafer bonding recipe in accordance with the physicalparameter of the second post-bond wafer; and bonding a fifth wafer to asixth wafer in accordance with the second wafer bonding recipe to form athird post-bond wafer.

Example 14. A processing system including: a non-transitorycomputer-readable storage medium including instructions that whenexecuted cause a processor of a computing device to perform operationsin coordination with a semiconductor wafer fabrication process, theinstructions including: having a first wafer bonding recipe and a modelof a wafer bonding process, the model including an input indicative of aphysical parameter of a first wafer to be bonded to a second wafer andconfigured to output a wafer bonding recipe based on the physicalparameter of the first wafer; obtaining measurements of the first waferto obtain the physical parameter of the first wafer; generating, by themodel, the first wafer bonding recipe based on the physical parameter ofthe first wafer; and bonding the first wafer to the second wafer inaccordance with the first wafer bonding recipe to produce a firstpost-bond wafer.

Example 15. The processing system of example 14, further including: aprocess chamber; a substrate holder in the process chamber, thesubstrate holder configured to mechanically support the second waferwhen the first wafer is bonded to the second wafer; and a batch systemto hold a plurality of wafers outside the process chamber.

Example 16. The processing system of one of examples 14 or 15, where theinstructions further include deriving the model of the wafer bondingprocess including: obtaining measurements of third wafer and fourthwafer to obtain a physical parameter of the third wafer and a physicalparameter of the fourth wafer; simulating wafer bonding of the thirdwafer and the fourth wafer in accordance with process conditions toestimate a physical parameter of a simulated post-bond wafer; andcreating the model of the wafer bonding process in accordance with thephysical parameter of the third wafer, the physical parameter of thefourth wafer, the process conditions, and the estimated physicalparameter of the simulated post-bond wafer.

Example 17. The processing system of one of examples 14 to 16, wherecreating the model of the wafer bonding process includes: comparing thephysical parameter of the third wafer, the physical parameter of thefourth wafer, the process conditions, and the estimated physicalparameter of the simulated post-bond wafer; and determining the model ofthe wafer bonding process in accordance with the comparison.

Example 18. The processing system of one of examples 14 to 17, wheregenerating the first wafer bonding recipe includes: estimating, by themodel, post-bond distortions of the first post-bond wafer in accordancewith the physical parameter of the first wafer; tuning processconditions of the first wafer bonding recipe to optimize the estimatedpost-bond distortions of the first post-bond wafer; and generating thefirst wafer bonding recipe in accordance with the tuned processconditions.

Example 19. The processing system of one of examples 14 to 18, where theinstructions further include bonding a third wafer to a fourth wafer inaccordance with the first wafer bonding recipe to produce a secondpost-bond wafer.

Example 20. The processing system of one of examples 14 to 19, where theprocessing system includes a wafer bonding system.

While this invention has been described with reference to illustrativeembodiments, this description is not intended to be construed in alimiting sense. Various modifications and combinations of theillustrative embodiments, as well as other embodiments of the invention,will be apparent to persons skilled in the art upon reference to thedescription. It is therefore intended that the appended claims encompassany such modifications or embodiments.

1. A method comprising: having a first wafer bonding recipe and a modelof a wafer bonding process, the model comprising an input indicative ofa physical parameter of a first wafer to be bonded to a second wafer andconfigured to output a wafer bonding recipe based, at least in part, onthe physical parameter of the first wafer, the physical parameter of thefirst wafer representing information relating to topographical featuresof the first wafer; obtaining measurements of the first wafer to obtainthe physical parameter of the first wafer; generating, by the model, thefirst wafer bonding recipe based, at least in part, on the physicalparameter of the first wafer; and bonding the first wafer to the secondwafer in accordance with the first wafer bonding recipe to produce afirst post-bond wafer.
 2. The method of claim 1, further comprisingannealing the first post-bond wafer to produce a fusion bonded wafer. 3.The method of claim 1, wherein the physical parameter of the first wafercomprises out-of-plane distortions of the first wafer.
 4. The method ofclaim 1, further comprising deriving the model of the wafer bondingprocess comprising: obtaining measurements of a third wafer and a fourthwafer to obtain a physical parameter of the third wafer and a physicalparameter of the fourth wafer; simulating wafer bonding of the thirdwafer and the fourth wafer in accordance with process conditions toestimate a physical parameter of a simulated post-bond wafer; andcreating the model of the wafer bonding process in accordance with thephysical parameter of the third wafer, the physical parameter of thefourth wafer, the process conditions, and the estimated physicalparameter of the simulated post-bond wafer.
 5. The method of claim 4,wherein creating the model of the wafer bonding process comprises:comparing the physical parameter of the third wafer, the physicalparameter of the fourth wafer, the process conditions, and the estimatedphysical parameter of the simulated post-bond wafer; and determining themodel of the wafer bonding process in accordance with the comparison. 6.The method of claim 5, wherein generating the first wafer bonding recipecomprises: estimating, by the model, post-bond distortions of the firstpost-bond wafer in accordance with the physical parameter of the firstwafer; tuning process conditions of the first wafer bonding recipe tooptimize the estimated post-bond distortions of the first post-bondwafer; and generating the first wafer bonding recipe in accordance withthe tuned process conditions.
 7. The method of claim 1, furthercomprising obtaining measurements of the second wafer to obtain aphysical parameter of the second wafer, wherein the first wafer bondingrecipe is further generated based on the physical parameter of thesecond wafer.
 8. The method of claim 1, further comprising bonding athird wafer to a fourth wafer in accordance with the first wafer bondingrecipe to produce a second post-bond wafer.
 9. The method of claim 8,wherein the first wafer and the third wafer are part of a first waferlot, and the second wafer and the fourth wafer are part of a secondwafer lot, and wherein the first wafer lot and the second wafer lot areprocessed together in a semiconductor fabrication process flow.
 10. Amethod comprising: having a model of a wafer bonding process, the modelcomprising an input indicative of a physical parameter of a firstpost-bond wafer and configured to output a wafer bonding recipe based onthe physical parameter of the first post-bond wafer; bonding a firstwafer to a second wafer in accordance with a wafer bonding recipe, toform the first post-bond wafer; obtaining measurements of the firstpost-bond wafer to obtain the physical parameter of the first post-bondwafer; generating, by the model, the first wafer bonding recipe inaccordance with the physical parameter of the first post-bond wafer; andbonding a third wafer to a fourth wafer in accordance with the firstwafer bonding recipe, to form a second post-bond wafer.
 11. The methodof claim 10, further comprising annealing the first post-bond wafer toproduce a first fusion bonded wafer; and annealing the second post-bondwafer to produce a second fusion bonded wafer.
 12. The method of claim10, wherein obtaining measurements comprises scanning the firstpost-bond wafer to obtain the physical parameter of the first post-bondwafer.
 13. The method of claim 10, further comprising: obtainingmeasurements of the second post-bond wafer to obtain a physicalparameter of the second post-bond wafer; generating, by the model, asecond wafer bonding recipe in accordance with the physical parameter ofthe second post-bond wafer; and bonding a fifth wafer to a sixth waferin accordance with the second wafer bonding recipe to form a thirdpost-bond wafer.
 14. A processing system comprising: a non-transitorycomputer-readable storage medium comprising instructions that whenexecuted cause a processor of a computing device to perform operationsin coordination with a semiconductor wafer fabrication process, theinstructions comprising: having a first wafer bonding recipe and a modelof a wafer bonding process, the model comprising an input indicative ofa physical parameter of a first wafer to be bonded to a second wafer andconfigured to output a wafer bonding recipe based on the physicalparameter of the first wafer, the physical parameter of the first waferrepresenting information relating to topographical features of the firstwafer; obtaining measurements of the first wafer to obtain the physicalparameter of the first wafer; generating, by the model, the first waferbonding recipe based on the physical parameter of the first wafer; andbonding the first wafer to the second wafer in accordance with the firstwafer bonding recipe to produce a first post-bond wafer.
 15. Theprocessing system of claim 14, further comprising: a process chamber; asubstrate holder in the process chamber, the substrate holder configuredto mechanically support the second wafer when the first wafer is bondedto the second wafer; and a batch system to hold a plurality of wafersoutside the process chamber.
 16. The processing system of claim 14,wherein the instructions further comprise deriving the model of thewafer bonding process comprising: obtaining measurements of third waferand fourth wafer to obtain a physical parameter of the third wafer and aphysical parameter of the fourth wafer; simulating wafer bonding of thethird wafer and the fourth wafer in accordance with process conditionsto estimate a physical parameter of a simulated post-bond wafer; andcreating the model of the wafer bonding process in accordance with thephysical parameter of the third wafer, the physical parameter of thefourth wafer, the process conditions, and the estimated physicalparameter of the simulated post-bond wafer.
 17. The processing system ofclaim 16, wherein creating the model of the wafer bonding processcomprises: comparing the physical parameter of the third wafer, thephysical parameter of the fourth wafer, the process conditions, and theestimated physical parameter of the simulated post-bond wafer; anddetermining the model of the wafer bonding process in accordance withthe comparison.
 18. The processing system of claim 17, whereingenerating the first wafer bonding recipe comprises: estimating, by themodel, post-bond distortions of the first post-bond wafer in accordancewith the physical parameter of the first wafer; tuning processconditions of the first wafer bonding recipe to optimize the estimatedpost-bond distortions of the first post-bond wafer; and generating thefirst wafer bonding recipe in accordance with the tuned processconditions.
 19. The processing system of claim 14, wherein theinstructions further comprise bonding a third wafer to a fourth wafer inaccordance with the first wafer bonding recipe to produce a secondpost-bond wafer.
 20. The processing system of claim 14, wherein theprocessing system comprises a wafer bonding system.
 21. The method ofclaim 10, wherein the physical parameter of the first post-bond waferrepresents topological information of the first post-bond wafer.